Zhihua Guo,1,* Yijun Li,1,* Lingling Wang,1 Xinxin Lin,1 Jingjing Sun,2 Jing Yu,3 Li Mao,3 Xia Zhu,1 Ye Miao,4 Chenxi Li1 

1Department of Military Medical Psychology, The Fourth Military Medical University, Xi’an, People’s Republic of China; 2No.10 Outpatient Department of Internal Medicine, 986 hospital of The Fourth Military Medical University, Xi’an, People’s Republic of China; 3Department of Radiology, 986 hospital of The Fourth Military Medical University, Xi’an, People’s Republic of China; 4Department of Obstetrics and Gynaecology, Tangdu Hospital of The Fourth Military Medical University, Xi’an, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Ye Miao, Department of obstetrics and gynaecology, Tangdu Hospital of The Fourth Military Medical University, Xi’an, 710000, People’s Republic of China, Email 85913036@qq.com Chenxi Li, Department of Military Medical Psychology, The Fourth Military Medical University, Xi’an, 710032, People’s Republic of China, Email lichenxi_2021@126.com

Purpose: Patients with coronavirus disease 2019 (COVID-19) are predisposed to associated mental health problems, including intolerance of uncertainty (IUS), perceived stress (PSS), low sense of control, dysfunctional beliefs and attitudes about sleep (DBAS), insomnia, and impaired feeling of security. However, these mental health concerns have not been studied in a joint framework. This study aimed to investigate the relationships and putative causality among the aforementioned six variables and determine relatively important ones, indicating potential intervention strategies for the associated mental health concerns.
Patients and Methods: A total of 1015 inpatients with COVID-19 aged 18 years or older in the Shanghai shelter hospital completed validated self-report scales to assess relevant psychopathological constructs. Two network models, a Graphical Gaussian Model (GGM) and a Directed Acyclic Graph (DAG), were estimated based on collected cross-sectional data.
Results: The GGM network was reliably stable, highlighting five strongest associations such as the connection between IUS “Intolerance of uncertainty” and DBAS “Dysfunctional beliefs and attitudes about sleep”. IUS was identified as the most central node. The DAG network suggested the key triggering role of PSS “Perceived stress” for other downstream variables.
Conclusion: This study provided insights into the complex pairwise connections between the mental health concerns and the pivotal roles of intolerance of uncertainty and perceived stress. The study findings were discussed in terms of both theoretical and clinical implications that might serve for the intervention of psychological distress and promotion of mental health in patients with COVID-19 or similar epidemics.

Keywords: directed acyclic graph, dysfunctional beliefs and attitudes about sleep, intolerance of uncertainty, network analysis, patients with COVID-19, perceived stress